Ocean Systems Engineering

  • 제11권4호
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  • Pages.331-351
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  • 2021
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  • 2093-6702(pISSN)
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  • 2093-677X(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

Restoration of underwater images using depth and transmission map estimation, with attenuation priors

  • Jarina, Raihan A. (Faculty of Integrated Technologies, Universiti Brunei Darussalam) ;
  • Abas, P.G. Emeroylariffion (Faculty of Integrated Technologies, Universiti Brunei Darussalam) ;
  • De Silva, Liyanage C. (Faculty of Integrated Technologies, Universiti Brunei Darussalam)
  • 투고 : 2021.03.08
  • 심사 : 2021.09.03
  • 발행 : 2021.12.25
⟨ 이전 논문 다음 논문 ⟩

초록

Underwater images are very much different from images taken on land, due to the presence of a higher disturbance ratio caused by the presence of water medium between the camera and the target object. These distortions and noises result in unclear details and reduced quality of the output image. An underwater image restoration method is proposed in this paper, which uses blurriness information, background light neutralization information, and red-light intensity to estimate depth. The transmission map is then estimated using the derived depth map, by considering separate attenuation coefficients for direct and backscattered signals. The estimated transmission map and estimated background light are then used to recover the scene radiance. Qualitative and quantitative analysis have been used to compare the performance of the proposed method against other state-of-the-art restoration methods. It has been shown that the proposed method can yield good quality restored underwater images. The proposed method has also been evaluated using different qualitative metrics, and results have shown that method is highly capable of restoring underwater images with different conditions. The results are significant and show the applicability of the proposed method for underwater image restoration work.

키워드

참고문헌

  1. Akkaynak, D. and Treibitz, T. (2018), "A Revised Underwater Image Formation Model", Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, USA, June.
  2. Ancuti, C., Ancuti, C.O., Haber, T. and Bekaert, P. (2012), "Enhancing underwater images and videos by fusion", Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Providence, USA, June.
  3. Anwar, S., Li, C. and Porikli, F. (2018), "Deep underwater image enhancement". https://arxiv.org/abs/1807.03528.
  4. Bo, L. (2016), "An adaptive method of multi-scale edge detection for underwater image", Ocean Syst. Eng., 6(3), 217-231. http://dx.doi.org/10.12989/ose.2016.6.3.217.
  5. Bo, L. and Yan, L. (2012), "A method for underwater image analysis using bi-dimensional empirical mode decomposition technique", Ocean Syst. Eng., 2(2), 137-145. http://dx.doi.org/10.12989/ose.2012.2.2.137.
  6. Carlevaris, B.N., Mohan, A. and Eustice, R.M. (2010), "Initial results in underwater single image dehazing", Proceedings of the IEEE OCEANS, Seattle, USA, September.
  7. Chang, H., Cheng, C. and Sung, C. (2019), "Single Underwater Image Restoration Based on Depth Estimation and Transmission Compensation", IEEE J. Oceanic Eng., 44(4), 1130-1149. https://doi.org/10.1109/JOE.2018.2865045.
  8. Chiang, J. Y. and Chen, Y. (2012), "Underwater image enhancement by wavelength compensation and dehazing", IEEE T. Image Process., 21(4), 1756-1769. https://doi.org/10.1109/TIP.2011.2179666.
  9. Codevilla, F., Gaya, J., Duarte, A. and Botelho, S. (2016), "General Participative Media Single Image Restoration", Computer Vision and Pattern Recognition, arXiv:submit/1493653 [cs.CV] .
  10. Drews, Jr., Nascimento, E., Moraes, F., Botelho, S. and Campos, M. (2013). "Transmission estimation in underwater single images", Proceedings of the IEEE International Conference on Computer Vision Workshops, Sydney, Australia, December.
  11. Drews, P.L.J., Nascimento, E.R., Botelho, S.S.C. and Campos, M.F. (2016), "Underwater depth estimation and image restoration based on single images", IEEE Comput. Graphic. Appl., 36(2), 24-35. https://doi.org/10.1109/MCG.2016.26.
  12. Duarte, A., Codevilla, F., Gaya, J. and Botelho, S. (2016), "A dataset to evaluate underwater image restoration methods", Proceedings of the IEEE OCEANS, Shanghai, China, April.
  13. Fabbri, C., Islam, M.J. and Sattar, J. (2018), "Enhancing underwater imagery using generative adversarial networks", Proceedings of the IEEE International Conference on Robotics and Automation, Brisbane, Australia, May.
  14. Fu, X., Zhuang, P., Huang, Y., Liao, Y., Zhang, X. and Ding, X. (2014), "A Retinex-based enhancing approach for single underwater image", Proceedings of the IEEE International Conference on Image Processing, Paris, France, October.
  15. Galdran, A., Pardo, D., Picon, A. and Alvarez-Gila, A. (2015), "Automatic Red-Channel underwater image restoration", J. Visual Commun. Image Represent., 26, 132-145. https://doi.org/10.1016/j.jvcir.2014.11.006
  16. Garcia, R. and Gracias, N. (2011), "Detection of interest points in turbid underwater images", Proceedings of the IEEE oceans, Santander, Spain, June.
  17. Gould, R.W., Arnone, R.A. and Martinolich, P.M. (1999), "Spectral dependence of the scattering coefficient in case 1 and case 2 waters", Appl. Opt., 38(12), 2377-2383. https://doi.org/10.1364/AO.38.002377.
  18. Lu, H., Li, Y., Kim, H. and Serikawa, S. (2017), "Underwater Light Field Depth Map Restoration Using Deep Convolutional Neural Fields", Proceedings of the International Symposium on Artificial Intelligence and Robotics, Kitakyushu, Japan, November.
  19. Hautiere, N., Tarel, J.P., Didier, A. and Dumont, E. (2008), "Blind Contrast Enhancement Assessment by Gradient Rationing at Visible Edges", Image Anal. Stereology, 27, 87-95. https://doi.org/10.5566/ias.v27.
  20. He, K., Sun, J. and Tang, X. (2011), "Single Image Haze Removal Using Dark Channel Prior", IEEE T. Pattern Anal. Machine Intelligence, 33(12), 2341-2353. https://doi.org/10.1109/TPAMI.2010.168.
  21. He, K., Sun, J. and Tang, X. (2013), "Guided Image Filtering", IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(6), 1397-1409. https://doi.org/10.1109/TPAMI.2012.213.
  22. Jarina R.A., Emeroylariffion A. and Silva, L.C.De. (2019), "Review of underwater image restoration algorithms", IET Image Process., 13(10), 1587-1596. http://dx.doi.org/10.1049/iet-ipr.2019.0117.
  23. Jarina R.A., Emeroylariffion A. and Silva, L.C.De. (2020), "Depth estimation for underwater images from single view image", IET Image Process., 14(16), 4188-4197. http://dx.doi.org/10.1049/iet-ipr.2019.1533.
  24. Li, C., Guo, J., Chen, S., Tang, Y., Pang, Y. and Wang, J. (2016), "Underwater image restoration based on minimum information loss principle and optical properties of underwater imaging", Proceedings of the IEEE International Conference on Image Processing, Arizona, USA, September.
  25. Li, C., Chunle, G., Ren, W., Cong, R., Hou, J., Kwong, S. and Tao, D. (2019), "An underwater image enhancement benchmark dataset and beyond", IEEE T. Image Process., 29, 4376-4389. https://doi.org/10.1109/TIP.2019.2955241.
  26. Li, C., Guo, J., Chunle, G., Cong, R. and Gong, J. (2017), "A hybrid method for underwater image correction", Pattern Recog. Lett., 94, 62-67. https://doi.org/10.1016/j.patrec.2017.05.023
  27. Li, J.A., Skinner, K., Eustice, R. and Johnson-Roberson, M. (2017), "WaterGAN: Unsupervised generative network to enable real-time color correction of monocular underwater images", IEEE Robot. Autom. Lett., 1-8. https://doi.org/10.1109/LRA.2017.2730363.
  28. Liu, C. and Meng, W. (2010), "Removal of water scattering", Proceedings of the International Conference on Computer Engineering and Technology, Chengdu, China, April.
  29. Narasimhan, S.G. and Nayar, S.K. (2002), "Vision and the Atmosphere", Int. J. Comput. Vision, 48(3), 233-254. https://doi.org/10.1023/A:1016328200723.
  30. Panetta, K., Gao, C. and Agaian, S. (2016), "Human-Visual-System-Inspired Underwater Image Quality Measures", IEEE J. Oceanic Eng., 41(3), 541-551. https://doi.org/10.1109/JOE.2015.2469915.
  31. Peng, Y. and Cosman, P.C. (2017), "Underwater Image Restoration Based on Image Blurriness and Light Absorption", IEEE T. Image Process., 26(4), 1579-1594. https://doi.org/10.1109/TIP.2017.2663846.
  32. Peng, Y., Zhao, X. and Cosman, P.C. (2015), "Single underwater image enhancement using depth estimation based on blurriness", Proceedings of the IEEE International Conference on Image Processing, Quebec City, Canada, September.
  33. Peng, Y.T., Cao, K. and Cosman, P. (2018), "Generalization of the dark channel prior for single image restoration", IEEE T. Image, 27(6), https://doi.org/10.1109/TIP.2018.2813092 .
  34. Roser, M., Dunbabin, M. and Geiger, A. (2014), "Simultaneous underwater visibility assessment, enhancement and improved stereo", Proceedings of the IEEE International Conference on Robotics and Automation, Hong Kong, China, May.
  35. Schechner, Y. Y. and Karpel, N. (2005), "Recovery of underwater visibility and structure by polarization analysis", IEEE J. Oceanic Eng., 30(3), 570-587. https://doi.org/10.1109/JOE.2005.850871.
  36. Serikawa, S. and Lu, H. (2013), "Underwater image dehazing using joint trilateral filter", Comput. Elec. Eng., 40. https://doi.org/10.1016/j.compeleceng.2013.10.016.
  37. Shanmugam, P., Sundarabalan, B., Ahn, Y. and Ryu, J. (2011), "A New inversion model to retrieve the particulate backscattering in coastal/ocean waters", IEEE T. Geosci. Remote Sens., 49(6), 2463-2475. https://doi.org/10.1109/TGRS.2010.2103947.
  38. Shin, Y., Cho, Y., Pandey, G. and Kim, A. (2016), "Estimation of ambient light and transmission map with common convolutional architecture", Proceedings of the IEEE OCEANS Monterey, USA, September.
  39. Simon E., Lars C. and Andrea C. (2015), "Hierarchical rank-based veiling light estimation for underwater dehazing", Proceedings of the British Machine Vision Conference, Swansea, UK, January.
  40. Smith, R. and Baker, K. (1981), "Optical properties of the clearest natural waters (200-800 nm)", Appl. Opt., 20, 177-184. https://doi.org/10.1364/AO.20.000177.
  41. Solonenko, M.G. and Mobley, C.D. (2015), "Inherent optical properties of Jerlov water types", Appl. Opt., 54(17), 5392-5401. https://doi.org/10.1364/AO.54.005392.
  42. Tan, C.S., Sluzek, A., Seet, G.L., G. and Jiang, T.Y. (2006), "Range gated imaging system for underwater robotic vehicle", Proceedings of the IEEE Oceans, Singapore, May.
  43. Wang, K., Hu, Y., Chen, J., Wu, X., Zhao, X. and Li, Y. (2019), "Underwater Image Restoration Based on a Parallel Convolutional Neural Network", Remote Sens., 11, 1591-1611. http://dx.doi.org/10.3390/rs11131591.
  44. Wen, H., Tian, Y., Huang, T. and Gao, W. (2013), "Single underwater image enhancement with a new optical model", Proceedings of the IEEE International Symposium on Circuits and Systems, Beijing, China, May.
  45. Whitmire, A., Boss, E., Cowles, T. and Pegau, W. (2007), "Spectral variability of the particulate backscattering ratio", Optics Express, 15, 7019-7031. https://doi.org/10.1364/OE.15.007019.
  46. Yang, H., Chen, P., Huang, C., Zhuang, Y. and Shiau, Y. (2011), "Low complexity underwater image enhancement based on dark channel prior", Proceedings of the International Conference on Innovations in Bio-inspired Computing and Applications, Shenzhen, China, December.
  47. Yang, M. and Sowmya, A. (2015), "An underwater color image quality evaluation metric", IEEE T. Image Process., 24(12), 6062-6071. https://doi.org/10.1109/TIP.2015.2491020.
  48. Zhang, M. and Peng, J. (2018), "Underwater image restoration based on a new underwater image formation model", IEEE Access, 6, 58634-58644. https://doi.org/10.1109/ACCESS.2018.2875344.
  49. Zhao, X., Jin, T. and Qu, S. (2015), "Deriving inherent optical properties from background color and underwater image enhancement", Ocean Eng., 94, 163-172. https://doi.org/10.1016/j.oceaneng.2014.11.036.
  50. Zhishen, L., Tianfu, D. and Gang, W. (2003), "ROV based underwater blurred image restoration", J. Ocean Univ. Qingdao, 2, 85-88. https://doi.org/10.1007/s11802-003-0033-0
  51. Zhou, W., Bovik, A.C., Sheikh, H.R. and Simoncelli, E.P. (2004), "Image quality assessment: from error visibility to structural similarity", IEEE T. Image Process., 13(4), 600-612. https://doi.org/10.1109/TIP.2003.819861.